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sentiment-analyzer
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import torch
import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
from transformers import pipeline

analyzer = pipeline("text-classification",model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")

# model_path = ("../.venv/Models/models--distilbert--distilbert-base-uncased-finetuned-sst-2-english/snapshots/714eb0fa89d2f80546fda750413ed43d93601a13")

# analyzer = pipeline("text-classification",model=model_path)

# print(analyzer(["This production is good", "This product was quite expensive"]))

def sentiment_analyzer(review):
    sentiment = analyzer(review)
    return sentiment[0]['label']

def sentiment_bar_chart(df):
    sentiment_counts = df['Sentiment'].value_counts()

    # Create a bar chart
    fig, ax = plt.subplots()
    sentiment_counts.plot(kind='pie', ax=ax, autopct='%1.1f%%')
    ax.set_title('Review Sentiment Counts')
    ax.set_xlabel('Sentiment')
    ax.set_ylabel('Count')
    # ax.set_xticklabels(['Positive', 'Negative'], rotation=0)
    # Return the figure object
    return fig

def read_reviews_and_analyze_sentiment(file_object):
    df = pd.read_excel(file_object)

    if 'Review' not in df.columns:
        raise ValueError("Excel file must contain a 'Review' column.")

    df['Sentiment'] = df['Review'].apply(sentiment_analyzer)
    chart_object = sentiment_bar_chart(df)
    return df, chart_object

# result = read_reviews_and_analyze_sentiment("../Files/Sentiment_Analysis_Reviews.xlsx")
# print(result)
# df = read_reviews_and_analyze_sentiment('path_to_your_excel_file.xlsx')
# print(df)

demo = gr.Interface(fn=read_reviews_and_analyze_sentiment,
                    # inputs=[gr.Textbox(label="Input you text/review comment for analysis", lines =4)],
                    # outputs=[gr.Textbox(label="Sentiment", lines=1)],
                    inputs=[gr.File(file_types=[".xlsx"], label="Upload your review comment file")],
                    outputs=[gr.Dataframe(label="Sentiments"), gr.Plot(label="Sentiment Analysis")],
                    title="Sentiment Analyzer",
                    description="THIS APPLICATION CAN BE USED TO ANALYZE THE SENTIMENT BASED ON FILE UPLAODED.")
demo.launch()


# Assuming you have a dataframe `df` with appropriate data
# fig = sentiment_bar_chart(df)
# fig.show()  # This line is just to visualize the plot in a local environment